บทถอดความฉบับเต็มของ The Shift to Know Thyself: Beth White on Agency in the Age of AI

industry that's high turnover. So it really kind of sparked my thought of like, hey, if you have great senior leadership and and management and you really think through, you know, what you want your people to do with inside organizations and then value them for what they bring, you know, you can have a really amazing organization that delivers great customer experiences, great products and other services because you have the good people behind the scenes. Marnie Robbins: What I love about that is even talking about and I I I've just had a few of these conversations l lately and I n at at risk of getting us down the hospitality track, but is the the idea of mastery and autonomy and sense of f fulfillment of one's personal purpose within the hospitality industry can be really difficult. So I'm wondering when you were in there, w were there any insights as far as how you set it up to get that as you mentioned, retention, not just attracting but retaining in a in a very difficult retainment environment. Beth White: Yes, yes. I mean it's interesting 'cause that that actually is a good question in this particular, you know, restaurant organization group, they were able to do something pretty unique. When you were hired to work at one of the locations, you learned several skills all at once. You learned how to cook, you learned how to prep food. You waited tables. There was opportunities to do people management. And there's even opportunities to work with inside other parts of the organization. Like, you know, they opened up a hotel and they opened up a winery. And if you wanted to become a brewer, you could start serving beer and and you know really apprentice with other brewers. So they they had made it so that there was a lot of career path in an industry where a lot of people think you go in I'm a server and that's all I'm gonna be. No, it doesn't have to be that way, especially if you you you give people exposure to other types of jobs because sometimes people just don't know where where and what they're good at. So it's pretty interesting when you give people a chance. You learn real quickly what you like to do and sometimes you learn really quickly what you don't like to do. So Mindy Honcoop: Я I mean those are like those human touch points, right? It's like about really meeting the person where they're at and y it takes a level of care to do that in an organization and taking the time to care, which you know, I think is so much at the heart of the work that you even do, Beth, and working with like HR organizations, really trying to bring back yes, AI is about automating and efficiency, but not at the loss of care and humanity. and Especially, you know, in a time when, you know, how do you often you hear people talk about compliance and governance, but that doesn't have to be at the loss of humanity either. The way you talk about governance and compliance, I feel is often uniquely different than when we hear other people talk about it. Cause I think you are just a person of care and everything extends out of that. And so I love when I hear you speak about governance and and compliance, it comes from a place of care. How can you speak to kind of how you have really helped organizations shift their thoughts on governance and compliance to really be able to put humanity back into that conversation. Beth White: Yeah, I mean that's a that's a great, setup for, you know, the the way our society has evolved, when you become a more mature society, you have rules and laws and and pr practices to live by and it's just so that humanity can coexist with each other without total chaos, right? Well the same same thing as Mindy Honcoop: Ha ha ha. Beth White: same thing as within the workplace, right? I mean, there's a lot of reasons, you know, when you are in a role in HR. And you have to learn about certain types of employment and labor law practices within states or at a national or federal level, is because there's times of of bad behavior, let's face it. You know, where you know FMLA started because women were, you know, getting pregnant and wanting to have babies and getting let go of jobs and not having any protection to go back to their jobs. So what did the government do? They listened to the people and put some regulations. In place so that people have a better sense of loyalty and a time you know with the companies they work with, knowing that they can have family life and have a work life and it all can coexist. So the same as it is happening now in the world of AI, what we're seeing is that you know it's moving so fast, nobody even really knew what AI was many years ago. They might have used Google Maps or their Alexa. Or Siri devices, but they didn't really understand, you know, what the technology was, and more practically, how it was going to start to become massive tools that they can use with inside their workplaces. And so a knee-jerk reaction was just to let's just give people access to these tools, but there weren't any guidelines or rules to go by. And so then you get into a place of fear and concern. Well, what is this new thing? How do I use it? How do I operate? Nothing came with an operation man manual. It's like getting a very powerful chainsaw without any instructions, right? Because you can do a lot of damage with a chainsaw if you're not using it properly. But you know, just with AI, it's not like you can do terrible damage, but there's a fear to even use it unless you understand, well, how should I be using this? What are some of the practical guidelines? What are some things that I can have to outline my practices? So that's really where a good level of AI governance now comes into the workplace is when companies help their employees by saying not only are we providing you these tools so that you can be more efficient and productive and and really rethink through what work's going to look like in the future. But we're also going to provide you some guidelines so that

you understand, you know, the the good and the bad and there's not repercussions later, right? Marnie Robbins: I think that's where we saw a lot of the friction, especially when AI was first ramping up, is it was this really cool exploratory, innovative time where everyone was playing with AI. A lot of HR folks were saying, Hey, y Mindy and I always say, Just do AI, right? They just do Beth White: Yeah, yeah. Marnie Robbins: it. But but there was no Mindy Honcoop: Ha ha Marnie Robbins: no goalposts, no no rules of engagement, et cetera, or governance. And so then what left us with a bunch of people saying, hey, what about my job? What's next? How do how do I excel or not excel? So I'm wondering, are you seeing that shift a little bit? So we went from exploratory innovation, doing all the things to more of you know, as as Helena and Dave put it last week, that that stall piece of you know, kind of leveling off and getting more into the freedom within the framework, so to speak. Beth White: Right, right. Well, you know, it's been great to have evolved with AI over the last eight years because frankly, eight years ago when we were presenting MeBibot to companies, we said, Hey, how would you like to automate your employee support using you know this application that installs in Teams and Slack? And you get to provide guarded information that employees get, and you reduce the amount of support that comes into your people teams so that. They can go do other types of work that's really strategic for the business. Well, no one really even wanted to know what was behind the scenes. I mean, that was the early, you know, 2018 to 2022 timeframe where that was like, that's great. You know, let's do that. That makes total sense. This is automation we need. Now we fast forward to the generative AI stage, which is what we all most commonly think of when we think of AI, you know, Chat GPT. Claude, perplexity, co-pilot, those are types of tools that leverage a large language model and generative AI to give you this first of all, it was a blank box like what do you want to do today? And it was opening it up into such a big broad world that people didn't even know what to be where to begin, right? And so by having natural curiosity, which by the way is a superpower these days, being able to be okay with not knowing what something does but just give it a try in a safe and practical way you know you're able to kind of see how systems and tools in AI can work and boss possibly be you know more helpful to you and then once you start to layer it into into a workplace and you build in some AI govern governance and policies then companies start really you know Moving forward for in a in a little bit different way. Everybody's testing AI, but the problem that I think that you know Mindy's been along this journey with me too that we're seeing is people are working in these independent bubbles. Like I have my own instance of co-pilot, I have my own instance of chat GPT, I'm gonna do work for me, me, and that's the piece that I'm not quite comfortable with myself, is because one of the things organizational design is all about. Is team. Yet we're giving people tools that's all about the individual initially, and we're not guiding them to the light of what the team can do together. And so that's why we've been doing with MeBibot, we've been doing AI consulting work and AI trainings to take teams of people together, so individuals, instead of having them do exercises on their own, self-guided, which Is what many companies are pushing out these days. We're bringing people together within like departments or roles, and we're basically saying, all right, here's some of the ways within what you do you could consider using AI to help the overall team and ways that you can build things like a Gemini Gem or Claude Project that can be shared across the team or the organization to help everyone as a whole. And so that was always the goal with MeBibot is to have a solution that helps out the whole of the employee population. And so where things are getting stuck right now is not making that move to the literacy required to the team collectively working together, getting a culture to feel that it's okay to test, iterate, experiment, and fail at times, and not be afraid. Afraid of any kind of retribution. And right now it's this it we've we've we're not really instilling that because instead we're creating a lot of fear in organizations with a lot of the reductions. I mean when Marnie Robbins: Yes. Beth White: you have a all these reductions that are being pointed to AI, but what are they really caused from? It's it's more of a look, our organization is going to look different in the future. We know that we're going to create brand new roles in certain areas. Areas that didn't exist before, and certain roles may be merged into other roles. So there's a shift happening, but the fingers are getting pointed at AI being the culprit and the expense of AI when that's not really it. And so to me, the best advice I could give to people who are worried and concerned about their jobs is start to take thoughts into yourself: like what are you good at? What do you like to do? And then think about what you've been doing. now within the the company and what your job could look like in the future with the partnership of AI, which is going to be expected. You know that's what the C-suite is expecting because they know that they need to be kind of in this world of using the most modern technologies to be competitive in both how they are managing their business and operating expenses as well as you know generating sales and products. So it is what it is. And so the sooner you can start to think about what your role is going to be, as well as the rest of the team, the more you'll start to feel a little more secure with your job security. Mindy Honcoop: Okay, so there's like so much in what you just said. So good, so good. No, I love it. I love Beth

White: I know, I'm sorry. I'm sorry. Marnie Robbins: Right? my god. Yeah. Mindy Honcoop: it. I just like, because what I'm hearing is like definitely we've seen that, but then that's why you're passionate about how do we build a common language and create a common foundation for teams to start talking and speaking to each other about where on the continuum are they, both from an emotional level but also from a ca cap capability level. Level with AI. Because you need that common language to start being able to talk at the team level. And and so and that's why and I love that free resource that we built around the Clarity AI framework to start trying Beth White: Yes. Mindy Honcoop: to have some of these team conversations. And so, I mean, that is one huge piece of what you're mentioning. But when we don't take the time to build those foundations, then we get we are seeing like what you're said, there's a lot of fear. We're getting stuck in these less than scarcity mindsets and these quick term decisions where people feel like they have to have all the answers, but no one Does and without the common language to speak, we're just it's almost like we're digging deeper holes in these organizations and we're losing sight of the system. Like we're losing sight of systems thinking and the inability to speak about a system with these kind of short-term, and there's a tension, right? Because to everything you just said with like very agile practices, because we don't know, we have to, you know, understand and learn we and try new things to help us inform. Form, is this the right next step? But it's Beth White: Right. Mindy Honcoop: like with you have to be able to make some short term decisions in order to understand and get more insight into the long term. and so that tension of short term and long term is so felt right now. But without a dialogue to speak about it as a team and without thinking about the system, we do get stuck into these individual, Almost we're it's so n it's not just silos anymore, but I was Beth White: Yeah. Mindy Honcoop: realizing we were talking or like individual silos. We're so disconnected. We're little they're like individual bubbles. Yes. Beth White: Little bubbles. Yes. I feel like everyone's these little bubbles living in their own protective universe and and they're not, you know, penetrating the bubble of others, right? So, yeah. Mindy Honcoop: And because of that, what you just said, which we have also been seeing, is the because we are in the short term, we aren't thinking, like organizations aren't thinking about those jobs of the future. Like we see that a lot in the work. And but you have this amazing data because I think a lot of people are just feel like everything's gonna go away. But you have really great data that you've done that you bring in when you're doing the consulting to help people stay or shift, have a mindset shift to an abundance mindset about the future for them to be able to kind of open their eyes

and be able to speak about the future in a safer way. I I I and you talked about it a little bit, but I'd love to get into the data that you're seeing about that shift. Beth White: Yeah, you know, I mean it's been it's an evolving process, right? I mean, there's a lot of experts out there that collect data from pretty comprehensive surveys, so I always follow the the Gartners and the and the McKinsey reports, et cetera, that are put out there to kind of you know, describe what's happening. Gartner's always been on the forward thinking of they're typically in my mind too aggressive, meaning they think things are gonna change a lot faster than what reality Mindy Honcoop: Yeah. Beth White: is. Cause I remember years ago when we started Meebot they said, you know everyone will be using a chatbot by 2020. Well that didn't quite happen, right? You know, we also had a global pandemic in there and some other changes that that impacted our world. But the data, you know, that I see that is encouraging is that more instructional information is being provided to people with inside companies before than ever. I mean we've gone from, you know, what is this type of solution to I'm gonna test it out on my own and do it in the shadow AI, which is I'm just I don't have approvals to do this, but I'm doing it anyway. using my own instance of ChatGPT, for example, to now the companies are like we can no longer ignore that people want to at least try AI, so we have to put some structure in place, but they just kind of throw it out there. Now I think they're realizing a year of paying for software that nobody knows how to use. Is starting to have the backlash, and so that's where the investment is being made into more trainings and more consulting services from the outside world because frankly, people inside are not, you know, at the capacity level of those who have been working with AI for many many years, and so bringing in outside expertise is now being more common, it is another expense, but if you're going to pay for these tools, you Got to figure out how to start to get out some value, and value doesn't always have to be an ROI number metric, it can be even things like incrementally people feel more comfortable using AI as a coworker, or you know, employees feel a little more comfortable with the future direction of their company and the strategy using AI because they're getting exposed and trained, and they don't feel as vulnerable. So, retention of talent. Talented people's helping, you know, is sticking around longer. And then they'll start to be more of the ROI on overall AI, which is still in its infancy. I mean, there's some indications from many who are using AI to really crunch a lot of numbers and data that it's a quick win, and they're seeing a lot of time savings in that area. But some of the shifts that of course require behavior changes. changes are just taking a little longer because we're people, we're human beings, and and change is hard and we're asking people to make some pretty massive behavior changes. Marnie Robbins: And that's what a lot of our conversation with HR professionals, people pros and whatnot, we're we're we're like kind of stuck there holding the old playbooks around career development, around communication, and what you said about the the team aspect in AI to me is just like mind blowing next step where people's brains aren't even are even at at this point is how they communicate, how they collaborate. But bringing the human into the in the decisioning and the decision making into the AI adoption implementation training, I think is is paramount. And so from a a human perspective, particularly speaking to our HR and People Ops leaders, I I'm curious what you've seen from a standpoint of bringing the human skills back because they talk about the degradation of the human skills and that's one thing that I'm I'm worried about is do we do we become too reliant on AI that we forgot forget to as Mindy puts it we forget to people right and and so that's where you know I think you're probably seeing a lot of that from the consulting side. Beth White: You know, it's interesting. I think people are still all very paramount to everyone and how they're doing their work. They'd rather spend time working with people than trying to b basically justify an Excel spreadsheet or or migrate data from one thing to another. Is that's the kind of work that gets a little bit circular and and gets people kind of stuck and not able to move forward. but what you know I think the the Biggest thing is that people leaders and people ops and others that are in the profession really need to take a step back and think about is I came into this role for a certain reason because I have these types of talents and attributes. You know, what could I be doing differently that I am, you know, not doing today, or how could I see this whole entire role being different than it is today? And one of my main things that I think about are We've become victims of systems. Like we can't do X, Y, and Z because Workday doesn't support it, or we can't do this because of ADP, right? And it's really, they've made us kind of stuck to some of the things that they're doing, these vendors. But if you just take a step back and go, wait a minute, what's the end result? Well, we need to pay people, right? We need to provide them benefits, you know, but but really we need to really rethink, you know, what is the kind of benefit. Programming that would work for people and is best for our organization, and being able to really ask those deeper questions. And you know, are we really attracting and re and doing our best to retain that top talent? Are we, you know, what are we doing to provide that career counseling advice to keep people on a trajectory where they have a career path within side of our organization? So, you know, I think we just get kind of stuck in process and. cycles and we need to just take a step away and break

free and give ourselves the latitude to have creativity to problem solve right Marnie Robbins: one thing Danny Meyer says in the book Setting the Table, one of his favorite questions is who wrote the rule? And and to me Beth White: That's right. Yeah. Marnie Robbins: that's just gold because it gets down to first principles and you know really it's it's this is an assumption we make that a thing has to be a thing and this is why, but who wrote the rule? I I just always love that one. Beth White: Yeah, I love that too. And it's always been my kind of question to people, so why do you do that this way? You know, and if you just ask people to stop and think, well, that's what how I was trained to do it, or that's how it was being done before me. and maybe there's cultures out there that that's encouraged just to follow the same types of processes just because it's in place. But companies that are really thriving now are throwing out those rules and really th rethinking everything like you know even to the point of like what do what are we as a company what do we sell who are our customers how do we want to best service them you know what does it mean to have people that best service the comp customers and build the right products to solve the problems that we want to solve as a company so it's really taking that big holistic view that's frankly missing a little bit with inside organizations. Mindy Honcoop: And even being able to dream and think about what are the future jobs we're we're gonna need. And Beth White: That's right. Mindy Honcoop: th the research that you show in that, Beth, is that you've gathered is that there's actually and I can't remember how many years there's actually more net new jobs than the ones that we're automating and losing. Beth White: Right, right. So when you look at like a pyramid of how, you know, organizational change is likely to happen and this is, you know, it's part part facts and data, part, you know, crystal ball, a little bit, because we all have to visualize, Mindy Honcoop: Ha ha ha. Beth White: but there's you know, it's the administrative routine work jobs. and and tasks that really need to be examined today. Because that is the perfect work for AI. The types of work to your other question before about where do we go as humans for cognitive thinking and to make sure we're not, you know, becoming dumber because of AI. You know, it's it's applying what we know business wise about organizations and the way you know our society operates to guiding The AI. I mean AI is not anything unless it has people to guide it, right? Like I spend more time thinking and writing a prompt. you know, correctly to ask the AI tool I'm engaging with to do something, then I may have just opening up a Word document and starting to just freeform right. You know, because if you're really starting to think cognitively about the tasks, you know, the information that you want to provide, the specific task you need to get done, and the desired output, which is something most of us don't even think about. when we create, let's say, okay, I'm creating an employee handbook. Why? Well, because I we always did an employee handbook. Why? You know, it's because you have to give your con your employees something, you know, some guidelines to live by. Why? Well because there's leg, you know, it's like you're going down that thread and so if you put into you know chat GBT, Claude, you name it, I want to create an employee handbook that one employees want to read and reference or you know two that really reflects the Principles of the organization. Three, because I want to have a workplace that people want to come to, they want to have safety in this workplace, they want to feel like you know they they have the mutual respect of the organization, and then they need to know when they step out of line, you know. But we never really ask ourselves those outcome things when you're doing something like writing a policy or a task, right? And it makes you actually start to become a much more reflective thinker. if you're using AI properly. Marnie Robbins: Yeah, and it is what you said about getting down to the root cause of the why, first principles, like what are we what are we solving for today? And it the output is really interesting because when you and how it applies to what you were talking about of individual versus teams. Like I work Beth White: Mm-hmm. Marnie Robbins: with clients and so I'll have my own clawed instance and I will create an output and then someone internally will have their own clawed instance and that output is inherent Different. Same topic. So the the output Beth White: Mm-hmm. Marnie Robbins: is not gospel. It is based on the intentionality of the person putting in that information. And so that's been really interesting to me as far as from a company standpoint and understand what is that central point of truth for our company and those guide rails that you're talking about. And even as an employee, even with me be bot, how do I know what I'm looking up is true and right and to the company to the company's intention? And that's what I'm hearing a lot of in in this conversation as well. Beth White: Yep, absolutely. And I think companies, you know, know ultimately that in order to get the ROI value that they're gonna want to get out of AI, they they need to help, you know, educate and train people to, you know, this whole concept of prompt writing, getting them more familiar with outcomes, but it also takes strong leadership within companies to let people know what are those desired outcomes. People can't guess. I mean, if you're at a public Locally traded company, you know, you've got something out there that a lot of people don't even ever even turn to or read, which is your 10k reports that they have to publicly produce, which actually tells shareholders sometimes more than the employees know, but employees have access, it's public information, and in private

companies it's a little different because that information isn't shared externally. And so every company I know that has great leadership and management, it Least they have team meetings and and company meetings asking those kind of hard questions of your leaders, you know. Like if I am to reinvent a hiring process, you know, what is your thought, leader, of a desired outcome that would make what we're doing with this AI project a success, right? And so it really has to map up to the the higher levels because they see other bigger goals that sometimes don't get Mindy Honcoop: I I'll never forget a conversation that we recently had with another friend around how she was just trying to how she you had even like, well, you could, you know, use AI, your business license to actually pull the ten to to ten Ks. Am I saying that right? And Beth White: Yep, yep, ten K reports, yep. Mindy Honcoop: write the ten K reports and you know, prompt it to understand with the right role and context and everything to really help you rethink your own current job description. Based on where the company's saying it's needing to head and and what its goal towards with the outcomes that they're reporting to the public, and then be able Beth White: Mm-hmm. Mindy Honcoop: to say what guidance are the skills that are gonna decrease with AI within your role versus what is gonna be emerging and and what Beth White: Right. Mindy Honcoop: might you want to like how might you rethink and lean into that and how your role changes. they got so excited they hadn't thought about doing it that in that way. and Beth White: Right, right. Yeah. Mindy Honcoop: so those are some. Some ways in which, you know, if anyone's listening. But you know, if you're in a private company, you know, hopefully your leaders are doing a j a good job of being able to say, What is that North Star? Or if we don't know what that Beth White: Mm-hmm. Mindy Honcoop: North Star is, what is it that we don't know but we know to be true right now, where we need to move forward together to or we need to learn to understand if we're pivoting, how are we progressing towards our the outcomes as an organization? I think is really important because then you know, always thinking agile. I love how they say paint done. Because you know, you can have you can say that we're all our outcome is an apple, but it the if you if one person's if they apple, they're thinking jazz apple and the other person is thinking Granny Smith, those are two very different outcomes. And so really getting Beth White: Right, right. Yeah. Mindy Honcoop: clear on kind of what you're saying around painting done and if you know, what is it that we don't know to be true yet, that's also another area that Like we need to seek to understand then and and learn more so that we can get more clear on that. Beth White: Absolutely. And it's all moving so fast. I mean, let's be real Mindy Honcoop: I know. Beth White: about it. People are are moving fast. I mean,

roles are getting cut before they have the plan of what people who are left are going to be doing moving forward. But guess what? Don't wait for senior management. Go ahead and take some ownership and control over it yourself by like really thinking, especially if you're a leader of a particular department, like you're overseeing all people operations or benefits or you're the HR leader of the organization. You know what people you have to work, you know, work with in their talents. You likely understand the overall business objectives of what's expected of HR. Maybe it's around you know hiring, you know, getting certain types of roles filled faster or hiring people or making sure you have high performers and those are your goals. Well then how are you going to get to those goals and make sure those are common basically part of your prompts that you put into your business rules that are being designed within your safe you know instances of an AI tool, right? Because you are at that point sharing, you know, more confidential information about the business that you want to make sure you're using that information in a in a proper way with inside your org. Marnie Robbins: paint done, I love that by the way, but paint done is maybe the acquisition of knowledge that we just didn't have before so that we can move forward. And to your point, what is the definition of a high performer? Like getting back and taking it a step further of if we're reinventing the social contract of work. internally, Beth White: Mm-hmm. Marnie Robbins: externally, what does that look like for our organization? So for heads of people to be able to really dive down into that that paint and done of maybe paint and done is something completely new and completely out there. And sometimes that's the role of the leader to be like, I don't know what that is. But if you can give me some really good information and and of a visual and an idea of where we want to go next, that's my paint done. Beth White: and people leader, you know, I this is such an exciting time for people in leadership roles. I mean, you really have an opportunity to say, what attributes and skills and talents within people do I want to develop and encourage? What kinds of new talents do I want to bring into the organization? And how do I see the people and the technology all working together? I mean, it is like an organizational development type of dream. role you know if you embrace it but unfortunately there's a just a lot of uncertainty because there with AI comes a lot of power and it is moving exponentially fast so predicting how much AI can do in the future is an unknown for many of us right now. And not to mention let's bring some other hot topics onto the table. You know there's the concept of tokens which now we're all becoming familiar to with And what happens when you max out? What happens when companies like Anthropic and OpenAI start raising their rates? How is that going to impact the organization? What happens when data centers cannot be built fast enough because of a lot of the community involvement and those decisions with inside the US at least, right? I mean, if we start being able to produce the kind of AI power, you know, what what does that really do to our expectations of what wolves will look like in the future if things start to get either regulated or their strangleholds on things or they get too expensive. It's pretty complex, but guess what? You can put all those attributes into AI and use it to do this kind of modeling, right? You can use it to help you model for the future with the various different what-if scenarios. Mindy Honcoop: Right, 'cause as we offshore our work to these large language models, it's not like it comes each with an employment agreement. They they Beth White: That's that's right. Mindy Honcoop: they own the agreement and raising their own salaries. So basically, you know, if we were to think of them as employees doing the work, we've now given Beth White: Right. Mindy Honcoop: them the ability to set their rates. Beth White: Which is where we're at today. So it brings a lot of complexity. And that's Mindy Honcoop: Yeah. Beth White: why humans are still needed. People are still needed because we're still working through a lot of topics. I mean, there may be a point in time where AI becomes like your electricity bill. You know, it's really flat, it's standardized, it may be even regulated by your state or the federal government. There's just a a ton of unknowns, but think about like the internet. When we were all first getting access to the internet years ago, I remember that had a lot of variable rates and even your cell phone and how many minutes you spent on phone calls and this and that. So but then people have you know you get to a tipping point where enough's enough and it plateaus out. So if AI follows a trend of other types of technology like the mobile phone usage and the internet. Internet, we'll see it kind of get to a more potentially regulated state and and expected kind of rate structure versus where it is today. Marnie Robbins: I was originally gonna ask like what shifts are you seeing that other people may not be noticing? Which that seems like actually what what a a good answer would have been, but just in case, are you noticing any other shifts that other people may not be noticing right now? Beth White: Yeah, I mean I think the tokens is one of the things that everybody is starting to notice and the and what is a data center, how does it work, what is it consuming for our, you know, you know, not only economy, but you know, for our ecology and our climate. And so getting that more visible, I think it needs to kind of happen, and that's one of the topics I follow. I also follow, you know, how a AI regulations will continue to change and evolve. We host a group called Ethical AI for HR, which you know tracks a lot of different state regulations that are out there on AI standards, EU AI

Act, a recent White House change in the executive order on AI. I mean, this is a landscape that's constantly moving, and a lot of people are very much in the dark on what that all means. And then there's other areas that are starting to get very exciting on the super geek front. which is, you know, obviously, you know, the, you know, when A G is, you know, when computers are thinking for themselves, which we're this close to, but then there's the whole concept of quantum computing, you know, which is a hot topic in in defense and in in the areas of the government and you know, thinking through really big mathematical problems and equations and and so that's gonna be a whole another era but I think we'll start hearing more about quantum computing in the near future as well. Marnie Robbins: And Beth, if if you don't follow Beth already and and Me B Bot, I'm telling you you want to because they are on top of it with all the all the shifting, Mindy Honcoop: Yes. Marnie Robbins: you know, regulations, industry knowledge. That's one of the things that I've always just been a big fan of Me Bebot and and what and and Beth, I met you before the the company, but the the innate curiosity and understanding how it affects humans first. and then we'll we'll ground the technology second. Beth White: Well i it is. I mean AI is is is again moving at a pace where we do have to be concerned about humanity and our futures on this planet and our future children's livelihoods and roles and and you know what environment they are in. we also have to consider you know gender differentials and you know economic gaps of people who are the haves and the have nots, like people who had phones and didn't have phones and it was very similarly. So there There's, you know, it's being talked about, not just, you know, we follow a lot of news coming out of the United Nations. They had a recent conference on, you know, obviously AI and thinking through it at a world, World Economic, you know, council also has this as a hot topic. It's, you know, this is this is going to be so impactful on us, we all just don't even know or understand the trickle-down effect yet. But but don't be overwhelmed by it all because Marnie Robbins: Yeah. Beth White: We can all still do our own micro parts and pieces to you know help you plan a vacation using AI. Super helpful with that, or doing meal preparation planning, and and then just helping do some basic tasks was inside your work that otherwise would have taken you a long time. I know I've used it a lot to create presentations, to you know, create different types of documentation, things that would have taken me hours. now can be done in a matter of minutes so it's pretty exciting. Marnie Robbins: Well, Beth, the the voice of Calm Through the Storm. And so thankful that that you Beth White: Awww. Marnie Robbins: came came on as a guest for for the shift where can people find you, follow you? I know we've talked about it a little bit, but is there one specific place where people should be following you or yeah. Beth White: Yes, yes. I mean I'm most active on LinkedIn. So if you just go to LinkedIn and look for Beth White, MB Bot M-E-B-E-B-O-T, there's a couple, of course, Beth Whites out there. So but that's how you define where you you see me. And you know, I try to, you know, I post a lot of original thought pieces, and you know, share things that I found that are interesting, you know, talk about the world of AI and just people and workplace changes. So that's a good place. I'm looking toward getting a going a substack. I don't have that going yet, but it's been on my list. So yeah. So just to just to have some just longer pieces, you know, of of what I'm thinking about today type moments, right? Mindy Honcoop: And it all comes with heart and soul because Beth, you truly do care. Like you take the time Beth White: Yeah. Mindy Honcoop: when people reach out and ask questions and yeah. You're seven as a day three more eight days too. Beth White: Well, thank you, Mindy. I mean, and you do too, and so do you, Marnie, and you guys know that this shift is happening and and you know we're we're people are there like ourselves to help guide people. They don't have to go in on this alone, right? And there are ways to do it in a way where you're supported and you have expertise to help you along the way. And I know you both provide a lot of that guidance and expertise and advice to the clients you work with. Yeah. Mindy Honcoop: Thank you. That is our heart. We can't do this alone. We truly believe it has to be done with community. Beth White: That's right. For sure. For sure. So great to be here today. Thank you. Marnie Robbins: See y'all later. Beth White: Okay, bye.